{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "measured-liabilities",
   "metadata": {},
   "source": [
    "# Saving, Loading Qiskit Machine Learning Models and Continuous Training\n",
    "\n",
    "In this tutorial we will show how to save and load Qiskit machine learning models. Ability to save a model is very important, especially when a significant amount of time is invested in training a model on a real hardware. Also, we will show how to resume training of the previously saved model.\n",
    "\n",
    "In this tutorial we will cover how to:\n",
    "\n",
    "* Generate a simple dataset, split it into training/test datasets and plot them\n",
    "* Train and save a model\n",
    "* Load a saved model and resume training\n",
    "* Evaluate performance of models\n",
    "* PyTorch hybrid models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "speaking-glance",
   "metadata": {},
   "source": [
    "First off, we start from the required imports. We'll heavily use SciKit-Learn on the data preparation step. In the next cell we also fix a random seed for reproducibility purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "exposed-cholesterol",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from qiskit.circuit.library import real_amplitudes\n",
    "from qiskit.primitives import StatevectorSampler as Sampler\n",
    "from qiskit_machine_learning.optimizers import COBYLA\n",
    "from qiskit_machine_learning.utils import algorithm_globals\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder, MinMaxScaler\n",
    "\n",
    "from qiskit_machine_learning.algorithms.classifiers import VQC\n",
    "\n",
    "from IPython.display import clear_output\n",
    "\n",
    "algorithm_globals.random_seed = 42"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rural-mileage",
   "metadata": {},
   "source": [
    "We will be using two quantum simulators, in particular, two instances of the `Sampler` primitive. We'll start training on the first one, then will resume training on the second one. The approach shown in this tutorial can be used to train a model on a real hardware available on the cloud and then re-use the model for inference on a local simulator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "charming-seating",
   "metadata": {},
   "outputs": [],
   "source": [
    "sampler1 = Sampler()\n",
    "\n",
    "sampler2 = Sampler()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "careful-allowance",
   "metadata": {},
   "source": [
    "## 1. Prepare a dataset\n",
    "\n",
    "Next step is to prepare a dataset. Here, we generate some data in the same way as in other tutorials. The difference is that we apply some transformations to the generated data. We generates `40` samples, each sample has `2` features, so our features is an array of shape `(40, 2)`. Labels are obtained by summing up features by columns and if the sum is more than `1` then this sample is labeled as `1` and `0` otherwise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ceramic-florida",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_samples = 40\n",
    "num_features = 2\n",
    "features = 2 * algorithm_globals.random.random([num_samples, num_features]) - 1\n",
    "labels = 1 * (np.sum(features, axis=1) >= 0)  # in { 0,  1}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "reduced-injury",
   "metadata": {},
   "source": [
    "Then, we scale down our features into a range of `[0, 1]` by applying `MinMaxScaler` from SciKit-Learn. Model training convergence is better when this  transformation is applied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dirty-director",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 2)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = MinMaxScaler().fit_transform(features)\n",
    "features.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "julian-amount",
   "metadata": {},
   "source": [
    "Let's take a look at the features of the first `5` samples of our dataset after the transformation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "thorough-script",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.79067335, 0.44566143],\n",
       "       [0.88072937, 0.7126244 ],\n",
       "       [0.06741233, 1.        ],\n",
       "       [0.7770372 , 0.80422817],\n",
       "       [0.10351936, 0.45754615]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features[0:5, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "racial-aluminum",
   "metadata": {},
   "source": [
    "We choose `VQC` or Variational Quantum Classifier as a model we will train. This model, by default, takes one-hot encoded labels, so we have to transform the labels that are in the set of `{0, 1}` into one-hot representation. We employ SciKit-Learn for this transformation as well. Please note that the input array must be reshaped to `(num_samples, 1)` first. The `OneHotEncoder` encoder does not work with 1D arrays and our labels is a 1D array. In this case a user must decide either an array has only one feature(our case!) or has one sample. Also, by default the encoder returns sparse arrays, but for dataset plotting it is easier to have dense arrays, so we set `sparse` to `False`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "understood-ukraine",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40, 2)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = OneHotEncoder(sparse_output=False).fit_transform(labels.reshape(-1, 1))\n",
    "labels.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "statewide-symbol",
   "metadata": {},
   "source": [
    "Let's take a look at the labels of the first `5` labels of the dataset. The labels should be one-hot encoded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "german-agreement",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 1.],\n",
       "       [0., 1.],\n",
       "       [0., 1.],\n",
       "       [0., 1.],\n",
       "       [1., 0.]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels[0:5, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aquatic-toner",
   "metadata": {},
   "source": [
    "Now we split our dataset into two parts: a training dataset and a test one. As a rule of thumb, 80% of a full dataset should go into a training part and 20% into a test one. Our training dataset has `30` samples. The test dataset should be used only once, when a model is trained to verify how well the model behaves on unseen data. We employ `train_test_split` from SciKit-Learn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "about-ordinary",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 2)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_features, test_features, train_labels, test_labels = train_test_split(\n",
    "    features, labels, train_size=30, random_state=algorithm_globals.random_seed\n",
    ")\n",
    "train_features.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "critical-angel",
   "metadata": {},
   "source": [
    "Now it is time to see how our dataset looks like. Let's plot it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fifty-scottish",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAGdCAYAAAARybUcAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjMsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvZiW1igAAAAlwSFlzAAAPYQAAD2EBqD+naQAAZ5BJREFUeJzt3QlYVOX3B/CDIC4pmpqKgKKpLYr7vhSoqbln5IK55VKaBmqZWrnkmqlB5pJaLv2SNETrr4W5kRtqipr7kqC4bynkLsz/+b7TEIODDjozd5bv53mm4d65M7xMyD3z3vOe46bT6XRCREREROTicmg9ACIiIiIie8DAmIiIiIiIgTERERERkR4DYyIiIiIiBsZERERERHoMjImIiIiIGBgTEREREekxMCYiIiIiEhEPcQBpaWly9uxZyZ8/v7i5uWk9HCIiIjIDeoilpKRIiRIlJEcO687Fpaamyr1796z6PcjxuLu7i4eHh9nxo0MExgiK/fz8tB4GERERPYakpCTx9fW12uv/888/cvr0aRWIE2WWN29e8fb2Fk9PT3GKwBgzxYZ/WF5eXloPh4iIiMyQnJysJrYM53FrzRQjKEbw88wzz/DKMqXDB6W7d+/KpUuXJCEhQcqVK/fIKxcOERgbfskRFDMwJiIicizWDFaRPoEACEFxnjx5rPZ9yDHhdyJnzpxy8uRJFSTnzp37ocdz8R0RERE5PM4UU1ayk9/OwJiIiIiIiIExEREREZEeA2MiIiIiB7NgwQIpWLCgRVJQVqxYIfYsMDBQwsLCbPK9GBgTERER2ViPHj2kXbt24ghmzJgh/v7+auFa7dq1ZceOHTb92aKjo2Xs2LFiCwyMiYiIyOWlporExopERurvsU0iS5YskcGDB8uoUaMkPj5eKleuLM2aNZOLFy8+8Wub25ClUKFCVi35lxEDYyIiInJp0dEi/v4iQUEiISH6e2xjv1amTZsmAQEB8tRTT6la0P3791eNTDJDGgTq82I2FwErej5k9NNPP0m1atXU42XKlJExY8bI/fv3szWOPn36SM+ePeXFF1+U2bNnq5rR3377rcnjR48eLQsXLlTfF2kauMXGxkpiYqL6GoH2yy+/rMbz/fffy5UrV6Rz587i4+OjXhc/cyQ+nTwklQKz1xMmTJC33npLBcwlS5aUOXPmiCUwMCYiIiKXheA3OFjk9Gnj/WfO6PdrFRyjxNiXX34pBw4cUIHm+vXrZejQoUbH3Lx5U8aPHy+LFi2SLVu2yLVr16RTp07pj2/atEm6desmoaGhcvDgQfn6669VbjKeY467d+/Krl27pEmTJkbjwnZcXJzJ57z//vvSoUMHad68uZw7d07d6tWrl/74sGHD1HgOHTqkAvnbt29L9erVZdWqVbJ//37p27evdO3a9ZHpGlOnTpUaNWrI7t271YeGfv36yZEjR+RJMTDWAC/XEBERaQ/n39BQdEh78DHDPkxUanGexgxpUFCQmh1t1KiRjBs3TpYuXfpAKsJXX30ldevWVcElAuitW7emB5WYHUYg2r17dzVb/Morr6hcXQTI5rh8+bLqLFisWDGj/dg+f/68yefky5dPNdXIlSuXFC9eXN0ytmLGz9W+fXspXbq0atOMmWIE01WqVFFjHDhwoAqqM/+smbVo0UIFxGXLlpUPP/xQihQpIhs2bBCbB8YbN26U1q1bS4kSJcxeyYgpdEzj403CD4BPK67KHi/XEBERuaJNmx6cKc4cHCMzAcfZ2tq1a6Vx48YqcES6AGZRkXaAWWIDDw8PqVmzZvr2888/rypVYDYW9u7dK59++qkKVg03pEVgFjfj69hSjRo1jLYReCNYRwoFcokxxtWrV8upU6ce+jqVKlVK/xrxKAJwS+Q9ZzswvnHjhkq8xgpFc6A3dcuWLdWnnj179qhPCr1791Y/tKux18s1RERErujcOcseZynIx23VqpUK/pYtW6bSGQxxF9IbzIWcZMwaI/4y3Pbt2yfHjh17ZGtkwCysu7u7XLhwQTLCNgLRx4Gc6Yw+//xziYiIULO+mPHFGJFi8aifE22eM0JwnJaWJk/KI7tPePXVV9XNXEjSxnQ5ckHghRdekM2bN8sXX3yhfnBX8ajLNehkics1bduKuLtrMUIiIsv8rcPsGgIJb2+Rhg35N43sF35HLXmcpSAQRpCH2MnQzthUagEW0e3cuVNq1aqltpFjizxjxFqAq/XYh6v1j8PT01OlaKxbty69/BrGhe0BAwY89HmYCTYHcqPbtm0rb775ZvrrHz16VC3000K2A+PsQnJ2xqRtQED8sELNd+7cUTeD5ORkcaXLNYGBthwZEZFl4KrXkCGY7fpvH1LFMC/Svr2WIyMyDR/cfH31V25NTVxh0gqP4zhruH79upohzahw4cIqkEX+8PTp01X6KoJHTDSamjVFTi4W6SGtAsFqnTp10gPlkSNHqplnVG0IDg5WQTbSK7DIDTnL5hg8eLDKUUYKBF43PDxcZQ+gSkVWkBeNzAAE5fh5ChQokOWxqKgRFRWlcqOffvppVQUDM9JaBcZWX3yH5GxTSdsIdm/dumXyORMnTlRvouGGMiWOzl4v1xARWTJVLCAAEyIiKSn6e2wzVYzsFa5mRET8FwRnZNgOD7feVQ+swapatarRDakPSFlFgPjZZ59JxYoVVVkzxEaZobwZUhBCQkKkfv36Kj8X5dAyTkSuXLlSfvvtN5WLjKAZV+xLlSpl9hg7duwoU6ZMUUE2FsghkI+JiXkgtssIeczPPfecCqafeeYZFdhn5eOPP1Yz2xgryrIhRUPLxiduOp2pz0hmPtnNTZYvX/7QH6B8+fLqU8Xw4cPT9/3yyy8q7xiJ31i5aM6MMYJjfLLy8vISa8CnFSRto9wHfi5LQ/UJLLR7FCyo5IwxETkSXDHFlVoEwViP/e+VXwUpfzhF7N8vcuwY0ypcDc7fmOCy5vkb5b6wnglpm+bkzZqCD25Id8x4ZRdzcgiKebXD8WXnd8TqqRSI/E0lbeMfiKmgGFC9AjdbOXPmjCoSnZKSohK/582b99Bpf0e8XENEZC1IAUP6BEpQZgyKAduYF0EZU6aKkb1C8Is1PsyPJ6unUqC2HpK0M1qzZo3aby9Qeg6XLpCrg5ljTOkjmd2ZLtcQEVmLIQWsYkXTjxv2M1WM7BnOv/jg1rmz/p7nY9eU7cAYpT8MJT8AU9P42lBvDikT6LJi8M4778iJEydUt5bDhw/LzJkz1crKQYMGib1A6gTGg2oZSBjHeNGlBeVDniDTxOQn0qgoER8f4/2YKcZ+Xq4hIkdkWLGPdAlTDPttvbKfiMjqOcZIFEdN4sywYhGNO3r06KHq7+G4jM9B4Il2hL6+vvLJJ5+o4+wpR8kAZU569eol0f+uFEH+9I8//qhWe1oKyxkRkTNhjjE5eo4xObfb1swxxorBh8XSprra4TnoZe0I0DEG6RQopD1kyBA1g2zJoDjj5RoiImeAv2koyYbqEwiCkVOM9AkEw1hIv3Kl/qoYg2IisndWX3zniJBagVqAL730kmqvaIBPvGjLaCi2TURExqliqGOMhXYGpUszVYyIHAcDYzP7cKO7DIpk45IQZsXRJpGIiP7Dlf1E5OgYGJspPj5e/vjjD1VfGQW4IyMjpUGDBloPi4jIrjBVjIgcGXMCzIQ2iDt27FANS06fPq3yptGFBj29iYiIiMjxMTDOZmrFrl275M0335TU1FQZMWKEvPrqq6pjHhEREZGtIK0TBQMssa5qBcrJ2LEePXrYrE00A+NsQh/yRYsWyTfffKM696H/eNeuXbUeFjl4qStUN0TXMNxjm4iInJstg70nsXHjRmndurVqhmZuED169GipUqWKxcaAvhKmqp5ZAwPjx4BfDLSQRs4xUizwP4zocaBctr+/CEqDh4To77H9bxltIiKykdS0VIlNjJXIfZHqHtskcuPGDalcubIqY2tp9+7dM+s4FD6wxOy4ORgYP4EKFSrItm3bjEq6/fDDD3L+/HlNx0WOAcEv6r6ePm28/8wZ/X4Gx0REthF9KFr8I/wlaGGQhESHqHtsY79Wpk2bJgEBAfLUU0+Jn5+f9O/fX3UfzgwzuOXKlVONK5o1ayZJSUlGj//0009SrVo19XiZMmVkzJgxqtKWuV599VUZN26cvPbaa2Ydj5ldfI+9e/eqiUTcDLO9+HrWrFnSpk0b9XONHz9epaaisRqab+BK/HPPPffAhGPm2XWs83rvvfdUV+VChQpJ8eLF1Sy1JTAwfkL4n5zxckOXLl3U5YN169ZpOi6yb0iXCA0VMdUrx7AvLIxpFURE1obgN3hpsJxONp6lOJN8Ru3XKjhGz4Qvv/xSDhw4IAsXLpT169erQDCjmzdvquASKZ5btmxR3Xs7deqU/vimTZukW7duEhoaqroPf/311ypIxXOspWPHjqpBGiYPz507p27YZ4AAFkH2vn371NV3FDFAV2R0GcYYR44cqdZwLV269KHfB+8Jguvt27fL5MmT5dNPP5U1a9Y88fgZGFtQ0aJF1S/ChQsX5JVXXlH/c7PzqYxcB+q8Zp4pzhwc40M/jiMiIutAukRoTKjo5MFZCsO+sJgwTdIqwsLCJCgoSHXgbdSokZq1zRwsIhXhq6++krp160r16tVVsLh161ZVRQswczts2DDp3r27mi1GbDJ27FgVIFtLnjx51HosdA3GTC5u2GcQEhIiPXv2VOMpWbKk5MyZU42zRo0aatYYE4x4/FGBMQoijBo1Ss2WI/jH8y0xKcnA2IKQUoFPLn379lVts/HL17hxYzl79qzWQyM7g+YHljyOiIiyb9OpTQ/MFGcOjpOSk9RxtrZ27VoVQ/j4+Kiuu1jof+XKFTVLbIDgs2bNmkZxCHJxDx06pLaRzoCZVASqhlufPn3ULG7G17GlGjVqPLAP+csI7J955hk1xjlz5sipU6fMbsIG3t7eFqkSxsDYwvCpCJ/E0AAE/3ORXoGk9dWrV2s9NLIj6AhmyeOIiCj7zqWcs+hxlpKYmKi67SL4W7ZsmSoVa1j8dvfuXbNfBznJmI3ds2dP+g0pDMeOHVM5x1p46qmnjLaxNuv9999Xecao9IUxYsb4UT8nZpozp7ZaorcEO99ZCXJ88OkHeTW7d++WEydOaD0ksiNok+vrq19oZyrPGKnreBzHERGRdXjn97bocZaCQBhB3tSpU1WuMZhKLUC65s6dO1WFLDhy5IjKM37hhRfUNhbdYV/ZsmVtOn5PT0+1qM4cyI2uV6+eWlxo8Ndff4lWGBhbEfJekOuzePFi9enHAGkWGRftkWu2zcWiW1SfwK9CxuDY8KsRHq4/joiIrKNhyYbi6+WrFtqZyjN2Ezf1OI6zhuvXr6sZ0owKFy6sAlnkD0+fPl3VEEbwOHv2bJOzpgMHDlSL9JBWMWDAAKlTp056oIy1Tph5Ri5vcHCwCrKRXrF//36Vs2zurPPx48fTtxMSEtSYUQ0Cr2sK8qINx2FhHVJBcuXKlWWshMWDuLKOHOPvvvtOlcPF11pgKoWV4VIFVl0aAmF8ksMv7P/93/9pPTTSWPv2IlFRIj4+xvsxU4z9eJyIiKzHPYe7RDSPSA+CMzJshzcPV8dZQ2xsrFStWtXohtQHpGCiXNtnn30mFStWlO+//14mTpz4wPPz5s0rH374oVrQVr9+fZXCuWTJkvTHUb5t5cqVKkUBucgImr/44gspVaqU2WPcuXNn+thg8ODB6msE3Vl5/fXXpXnz5mrxIPKGkV6albffflvat2+vrrDXrl1b5VFnnD22NTcdpi/tXHJysirujE9WXl5e4sg++ugjmTBhQvovF37RccmBXBeuNqH6BBbaIacY6ROcKSYiZ2CL8/ft27fV7CRmGB83bxYl2VCdIuNCPD8vPxUUt3+BsxSOLju/IwyMbQzJ5Ph0F47r5CJq9hif7nDZgYiIyJk4SmAMKMmG6hNYaIecYqRPWGummGwrO78jTKWwMcwO4zIGOtWgpApqDeKSxPLly7UeGhERueAVq9hYEVzpxr0rNxVCEBzoHyidAzqrewbFromBsUbatm2rktKR74O8Y+TXzJs3T+thERGRi0DbeVysDApC0wX9PbbZjp5cGQNjDSH5HXWOP/jgA9UHPWMfcCIiImtB8IuqOJk7cKKEJPYzOCZXxcBYYyi1gh7fKJ1SpEiR9P2Gdo5ERESWhHSJ0FDTNdQN+8LCXDutglwXA2M7kXFRAnqdo2QJypUgYZyIiMhSUAUn80xx5uA4KUl/HJGrYWBsh5KSklTd41mzZqkc5KNHj2o9JCIichIoDWnJ44icCQNjO/Txxx9LTEyMKoqNDjVoLY3ueURERE8K9dIteRyRM2FgbKeaNm2qqlYEBgaqdoxdunSR3r17y82bN7UeGhEROTA0EUKHTUP7+cyw389PfxzZrwULFqiyr08KV6hRQpb0GBjbsRIlSsjatWtl1KhR6hf322+/5aI8IiJ6IuisGaHvgvxAcGzYRg8qduC0rh49ejhMNaoZM2aoRmRojoE1UI+KRazxs40ePVqqVKki1sbA2M65u7urXwYEyKhegRlkIiKiJ9G+vUhUlIiPj/F+zCRjPx53NWx2Yhq68w4ePFhN0sXHx0vlypWlWbNmcvHiRXFGDIwdRKNGjeT9999P3z5x4oSqWnHjxg1Nx0VERI4JwW9iosiGDSJYxoL7hATXDIpRt7lsWeNmJ9jWsp7ztGnTJCAgQJ566inV6wDnfKRWZoY0iHLlyqnZXASsWMCf0U8//STVqlVTj5cpU0bGjBkj9+/fz9Y4+vTpIz179pQXX3xRZs+eLXnz5lVXsU3BZB6qa+H74mo3brH4pPFvcYEOHTqoFJBChQqpZmeJ+CX8F46rVauW+plxTP369eXkyZMqbQTjxrorw2tinzUwMHZAOp1O5RyjakWNGjVk3759Wg+JiIgcENIlcCGyc2f9vSumTxianQQEiMTFiaSk6O+xrWWzkxw5csiXX34pBw4cUIHm+vXrZejQoUbHYN3R+PHjZdGiRbJlyxbVSbdTp07pj2/atEm6desmoaGhcvDgQfn6669VQInnmOPu3buya9cuadKkidG4sB2HN8kETOIh+G3evLmcO3dO3erVqyf37t1TgXv+/PnVuDDefPnyqePwfRCsI/3i5Zdflj///FO9ft++fVUQ3LFjRxkyZIhUqFAh/TWxzyp0DuD69esoOa7uSe/333/XlShRQr0vuXPn1s2dO1eXlpam9bCIiIhsev6+deuW7uDBg+o+u+7f1+n8/XW61q11utRU48ewjf2lS+uPs7Tu3bvr2rZta/bxP/74o65w4cLp2/Pnz1fv7bZt29L3HTp0SO3bvn272m7cuLFuwoQJRq/z3Xff6by9vdO3cfzy5ctNfs8zZ86ox7du3Wq0/4MPPtDVqlUrWz8bvu9zzz1nFKvcuXNHlydPHt3q1at1V65cUd8rNjbW5GuOGjVKV7lyZZ21f0c4Y+ygXnrpJVW1Ap+00AQElznefPNNScFHXSIiInokNDHBlfwRIzATavwYtocP16eXaNHsBGuLGjduLD4+PmqWtWvXrnLlyhWj6lQeHh5Ss2bN9O3nn39epSAcOnRIbSP14NNPP1Uzs4Yb4gXMuNq6ytXevXvl+PHj6mcxjAXpFIhh/vrrL/U1Fu1hVrl169YSERGhxmlrDIwdGOocr1q1Sj777DO1SA+1jlHzOGO+DhEREZlmiLsqVjT9uGG/reMznMdbtWollSpVkmXLlql0BlSGAKQdmAs5ycjNxUSa4Yb0y2PHjqmc40cpUqSIii8uXLhgtB/bxYsXz9bPhLEgRsk4FtzQxCwEid0iMn/+fJVCgdQLLPorX768bNu2TWyJgbGDQ64Pco42btyokvPRWtqbVdmJiIgeyXC63L/f9OOG/bY+rSIQTktLk6lTp6oOuAgQz549+8BxyMvduXNn+vaRI0dUnvELL7ygtrHoDvvKli37wA3xw6N4enqqYHbdunXp+zAubNetW/ehz0vNVNYDY0FAXrRo0QfGUqBAgfTjqlatKsOHD5etW7dKxYoV0xucmXpNa/Cw+ncgm8CnK3zywieyXLlypf+DwaUSBMtERERkDE1M/P1FJkxAdQfjdIq0NJGJE0VKl7Zes5Pr16+rc3dGhQsXVsEiFqtNnz5dpRVgoRqqQWSWM2dOGThwoFqkh7SKAQMGqEAalR1g5MiRaua5ZMmSEhwcrIJhpDTs379fxo0bZ9YYBw8eLN27d1eL/fG64eHhqiIWqlRkBTWPV69erYJy/DwIfFE04PPPP1eVKJDe4evrqypOREdHqwk+/Lxz5syRNm3aqD4OeC4CaSweNLxmQkKCer/wXKRkGOIdi9I5AC6+ezwjRozQlSlTRrdz506th0JERC7I3hffwbJlOp2bm36hHdaYJSfr77GN/XjcGrBADe9N5luvXr3U49OmTVOL5LA4rVmzZrpFixapx//+++/0xXcFChTQLVu2TJ3rc+XKpWvSpInu5MmTRt8nJiZGV69ePfU6Xl5eatHcnDlzzFp8ZzB9+nRdyZIldZ6enur5GRf8mXLx4kXdK6+8osuXL596/Q0bNqj9586d03Xr1k1XpEgRNV6Mu0+fPur34/z587p27dqpnxnfp1SpUrqRI0fqUv9dFXn79m3d66+/ritYsKB6Tfz81vgdccN/xM4lJyerTxv4ZMXZT/Pg0xxyk1DvGJcfpkyZoj5JouwJERGRs5y/sXgLM4mlS5c2K2/WFJRkGzJEvxDPADPFU6a4Zl1nZ5Od3xHmGDspFMdGjtJrr72mEvXfe+89ef311+Xvv//WemhERER2BcHv8ePGzU6OHWNQ7IoYGDsxlGzBalbkKGHWePny5Sqpffv27VoPjYiIyK6w2QkBA2Mnh9QJpFBgdeezzz6rEt1RIxCXtYgAi3zRrTMyUn9vg0W/REREdolVKVwEyq0gtQLtFVu0aGFUGoVcF/LqQkNFTp/+b5+vr0hEBC8hEhGR62Fg7EIQDP/www9GC/BQSBs1CevXr6/p2EiboDg4GJVpjPefOaPfHxXF4JiIiFwLUylcTMag+PLly/LGG2/Iyy+/LJMmTVIBMrkGpEtgpthUTRrDvrAwplUQEZFrYWDswlAYOzAwUHWSQZeZli1byqVLl7QeFtnApk3G6ROmguOkJP1xZDvM9yYi0hYDYxeGrjHfffedzJs3T/LkySMxMTFSpUoV1V6anNu5c5Y9jiyT2lK2rEhQkEhIiP4e29hPRES2wcDYxSG1olevXrJjxw7VWx292IOCglSrSKZWOC9vb8seR5bJ9w4IQN6/SEqK/h7b2M/gmIjINhgYk1KxYkX5448/pEePHiogRi91dslzXg0b6qtPZPW/GPv9/PTHkXUhXQIdt1q1ElmxQqROHZF8+fT32Mb+999nWgURGVuwYIHqV/CkcK5fgT82pDAwJqNuefPnz1eVK+bOnZseGDtA13DKJhSuR0k2yBwcG7bDw1ng3haQx402tCNGiOTI9BcZ28OHiyQkMN+byNlgIqpdu3Zi7zZu3CitW7eWEiVKmB1Ejx49WqVm2uMHgUdhYEwP6NixY/ovH4Lizp07y8iRI9UiPXIeKMWGkmw+Psb7MZPMUm22Y8jjrljR9OOG/cz3JrKu1LRUiU2Mlch9keoe2yRy48YNqVy5ssyYMUNcAQNjeqjff/9dlixZImPHjpXGjRurHGRyHgh+MVu5YYPI4sX6e8xOMii2HUMe9/79ph837Ge+t2WxAghlFH0oWspOLytBC4MkJDpE3WMb+7Uybdo0CQgIUFdz/fz8pH///vLPP/88cBxmcMuVKye5c+dWnW2TUFIog59++kmqVaumHi9TpoyMGTNG7t+/b/Y4Xn31VbXu6LXXXjN7Zhffw5CSiRv2wbVr16R3797yzDPPiJeXlzRq1EgdZ4Cvsc4JxQHwOJqT7dy5U2JjY6Vnz56qa6/hNTErbQ0MjOmhUM5t8eLFki9fPhUk41Pj6tWrtR4WWRDSJQIDRTp31t8zfcK2kMft7y8yYYJI5vWu2J44UaR0aeZ7WxIWM+I9z1gBBNtc5OiaEPwGLw2WgKIBEtcrTlKGp6h7bGO/VsFxjhw55Msvv5QDBw7IwoULZf369TJ06FCjY27evCnjx4+XRYsWyZYtW1Tg2alTp/THN23aJN26dZPQ0FA5ePCgfP311ypIxXOsedV5yJAhUqFCBTl37py6YR+gd8LFixfl119/Vd14EbBj0u3q1avq8S5duoivr69a84THhw0bJjlz5pR69epJeHi4CpYNr/k+Fl9Yg84BXL9+HUmu6p60ceTIEV2VKlXU/wfchg0bprt3757WwyJyCsuW6XRubjpd69Y63datOl1ysv4e29iPx8my77W+Wvd/N+zje+2Y5+9bt27pDh48qO6z637qfZ1/uL+u9eLWutS0VKPHsI39pcNLq+MsrXv37rq2bduaffyPP/6oK1y4cPr2/Pnz1Xu7bdu29H2HDh1S+7Zv3662GzdurJswYYLR63z33Xc6b2/v9G0cv3z5crPGIGYeO2rUKF3lypWN9m3atEnn5eWlu337ttH+Z599Vvf111+rr/Pnz69bsGCBydfEz1ugQAGdtX9HOGNMZilfvrxqH41LOYBOefhkR0SWy/fet0+kXj0RLy/9PdIomO9tOez4SJltOrVJEq8lyoiGIySHm3FIhO3hDYZLwrUEdZytrV27Vs2m+vj4qNSCrl27ypUrV9QssYGHh4fUrFkzffv5559Xa4QOHTqUnprw6aefqqu+hlufPn3UjGvG17GFvXv3qlSQwoULG40nISFB/vrrL3XM4MGDVapFkyZNVJxh2G9LHjb/juSwkJ+E5HukV7zzzjvy7rvvaj0kIqeB4LdtW331CSy0Q04x0ieY2qJNx0ekFZHzO5eiX9Vasajp1a+G/YbjbCUxMVFatWol/fr1U2kPhQoVks2bN6u+A3fv3pW8efOa9ToIRJHv297Ep2uc023pn3/+EW9vb5UvnJlhwT/yhkNCQmTVqlUq3WLUqFGqUpa5+c2WwMCYsg05Qs2bN1efYA2QHI/8Y+QCEdGT5XuTdbDjI2XmnV+/qnX/xf1Sx7fOA49jf8bjbAX5tegpMHXqVJVrDEuXLn3gOCyiw/m3Vq1aavvIkSMqzxgNuwA5vNhXFm00bcjT0/OBSlYYy/nz59Ustz+S+h9yhRq3QYMGqapYKCOLwNjUa1oDA2N6LBmD4sOHD6tZZDQJwSe7h/3CE5Frw3lNq1lxdnykzBqWbCj+Bf1lwqYJsqLTCqN0ijRdmkzcPFFKFyytjrMGVFnYs2eP0T6kGiCQvXfvnkyfPl3VEMbCutmzZz/wfExGDRw4UC3SQ8A5YMAAqVOnTnqgjFKrmHkuWbKkBAcHqyAbKQ379+9XlSbMnek9fvx4+jZSHzBmzGLjdU1BHGA4DovpEDMgPaJu3bqqdvPkyZNV8ItKV5gdRuCLxXoffPCBGmfp0qXl9OnTahHe66+/nv6aGMu6devURBxmzc2dOc8WnQPg4jv7tmbNGl3BggXV/yPcm5vET2RL9+/rdBs26HSLF+vvsU22hYVtvr7Gi96wbasFb/h/ju9navGdYQGenx9/N1xp8R0sO7hM5zbaTS2023pqqy75drK6xzb243FrwOI7w4L2jLdevXqpx6dNm6YWyeXJk0fXrFkz3aJFi9Tjf//9t9FitGXLlunKlCmjy5Url65Jkya6kydPGn2fmJgYXb169dTrYPFbrVq1dHPmzDF7Qd2GDRtMjhPjzwoW2L3++uvpsQHGCsnJybqBAwfqSpQoocuZM6fOz89P16VLF92pU6d0d+7c0XXq1Ent8/T0VMcMGDDA6P/rO++8oxYg4jWxwM8avyNu/74pdi05OVkKFCigPlmhVAfZn5MnT6pyLNu3b1fbKA3z2WefSa5cubQeGpEqw4W2y6jZbIALG1OncmGbrWZx8f8gOPjBhW+GTou2WmRoGAdkHIutx+EqbHH+vn37tpqdxCzj4+bNoiTbkN+GqIV4BpgpntJ0irR/gb8Qji47vyOPVZUCC7AwpY0Xr127tuzYseOhx6P23HPPPSd58uRRRaqRN4JBkvMoVaqUqpdoqCsYEREh9evX12RFKZGpQCggQCQuTiQlRX+Pbex39dq1+PmRfpixpi+2Lfm+2FM1CHZ8JFMQ/B4feFw2dN8gi9svVvfHBh5jUOyKdNn0ww8/qCnub7/9VnfgwAFdnz591FT5hQsXTB7//fffq+l93CckJOhWr16tLg0MGjTI7O/JVArHsnLlSl2hQoXU/7NPPvlE6+GQC8MlcX9/fT3gVOMSpWob+0uXdt1L5xnrJ8fF6XQpKfp7S9dPRuqKqdSFzDccZytMrbENR0ilIOd3y5qpFJghRs28r776Sm1j1SRmgZH8jQ4lmSERHPX0kCxtgI4ouOSO0iPmYCqF40FLyilTpqgVtVgQQKQFVAXCDChmiOs8uOBc7Ue9YLTCdrVqEJidxcwwZs5XrECXLeOOe+3a6esoHzv25GkVaLuM2ehHQVtydGAk5+EoqRTk3G5bK5UCtfNQQgQrC9NfIEcOtY3mD6agjR+eY0i3OHHihPzyyy/SokWLLL/PnTt31D+mjDdyLPiwhHQKQ1CM35233npLjh49qvXQyIUYym5VNF2iNH2/K5bnQk4xcq5HjDAOigHbw4dj9bn+uCfFahBE5CiyFRhfvnxZ1ZArVqyY0X5sozadKSjUjK4rDRo0UGVFnn32WVXaawT+Gmdh4sSJ6hOm4YYgixwbfgdQi7B69eqyGNNCRDZgCLQw82mKYb8rBmS2/NCAxXzI4TUscMsM+/FnHscREWnJ6i2h0eFkwoQJMnPmTImPj5fo6GhVs27s2LFZPmf48OHqsovhhsvy5NjQSvrll19WNQjRShotKW3djpJcDwItVJ+YMEGfHpARtrEfn/ORVuBqbYBt+aEBqRgREfqvMwfHhu3wcHb5oyfjAEW2yAF+N7IVGBcpUkTc3d3lwoULRvuxXbx4cZPP+eSTT1R/b/S+DggIUEWcEShjVhj5yaagxBdykTLeyLGVKFFC9X1HsXE3NzeZN2+eylc39HMnsgYEWijJtnKlPmc2Y1WKNm30+/HnDNlhCKBdqULFoz40TJwoUrq05WZxWQ2CrAVxiSFlj8gUw0ScOd15s7UqCu34cCkcC+nQuQQQ3GIbi+yyGoyhnWHmX2J+unMtyDdGz/aXXnpJzRqj806NGjVkyZIlqjMPkTUYAjLUMcZCO4PMa0LPnNGXb3OVIM3woQE/M/6cI6cY6ROYKUZQjA8NeC8sOYuL97VtW+0635HznlvQAe3SpUsq8Mkcc5Dr0ul0Kg69ePGiFCxYMD3+fJhsV6VAENO9e3f5+uuvVctB1ChG/260BUaucbdu3cTHx0fNCMPo0aNl2rRpMmfOHDVDiLaC/fr1UwE2XsscrErhfJCT/uabb6p2j0ixQe45kTUhVQJVKjp0ELl61fQxuKyPGUwsOnOVYM1U8xPMFE+Z4hofEMi6bHX+xmwxqg5kdSWaXFvBggVVZgOuWD9KtutoobsZPpXhkjiCmypVqkhMTEz6grxTp04ZfVr7+OOP1UBwf+bMGXnmmWdU3+/x48dn91uTE8Ev6OrVq9UHqoxBMRZ4ImWHyNIQ6OKWVVAMmCbAkgbMaLpK+TbO4pIzwBXtcuXKMZ2CHoCrCObMFBuwJTTZjTVr1qgcdJR5Q2k3cz7ZEWUH6+kS2RbP3+RomIhDduN///uf3LhxQy3UxILNFKySIrIg1tMlIqKHYWBMdgN1jidNmqQueXz//fdqYd7evXu1HhY5EdbTJSKih2FgTHYDuekffvih/P777+Lr66u65GHB5uzZs1nBhCyC9XSJiOhhGBiT3alfv77s2bNHlXBDe3BUMVm/fr3WwyInwXq6RESUFS6+I7uFX80vvvhCDh48KHPnzuViPLJ4+TZWYiCyLp6/ydEwMCa7h19RQ1B85coVWbZsmWopzUCZiMi+8fxNjoapFGT3DAEwAmQ0l3n77bfl9ddfl7///lvroREREZETYWBMDqVp06aqWPfy5culWrVqsn37dq2HRERERE6CgTE51Mzxe++9J1u3bpUyZcpIYmKiNGjQQLUcd4CMICIiIrJzDIzJ4aC+cXx8vLzxxhty//59GTJkiLRp00auPqzXLxEREdEjMDAmh4TFHEuWLJFZs2ZJrly5ZP/+/aoOMhEREdHj8njsZxLZQWrFO++8I3Xq1JHU1FQpWLCg2o+0CtwYKBMREVF2MHIgh1elShWpXr16+vacOXOkZcuWcunSJU3HRURERI6FgTE5lZSUFBk+fLjExMSogBntpYmIiIjMwcCYnEr+/Pll48aN8vzzz8vZs2elUaNGMnbsWJVqQURERPQwDIzJ6VSsWFF27typmoGkpaXJyJEjpVmzZnL+/Hmth0ZERER2jIExOaWnnnpKFixYoG558+aVdevWqTzkf/75R+uhERERkZ1iVQpyapg1rlmzpnTs2FHd8uXLp/WQiMgBIRtr0yaRc+dEvL1FGjYUcXfXelREZGkMjMnpvfjii7Jjxw7x9PRM33f8+HE1k1yiRAlNx0ZE9i86WiQ0VOT06f/2+fqKRESItG+v5ciIyNKYSkEuIU+ePOL+7/TOrVu3pH379qpqxerVq7UeGhHZeVAcHGwcFMOZM/r9eJyInAcDY3I5V65cUc0/UOe4efPmqrwbWksTEWVOn8BMsU734GOGfWFh+uOIyDkwMCaX4+vrK9u2bZN+/fqp7UmTJklgYKAkJSVpPTQisiPIKc48U5w5OMafDRxHRM6BgbGLSE1LldjEWIncF6nuse3KcufOLTNnzpQlS5aIl5eXbNmyRaVWrFq1SuuhEZGdwEI7Sx5HRPaPgbELiD4ULf4R/hK0MEhCokPUPbax39V16NBB4uPjVSm3q1evqtljnanrpkTkclB9wpLHEZH9Y2Ds5BD8Bi8NltPJxtcDzySfUfsZHIs8++yzasb4ww8/lMWLF4ubm5vWQyIiO4CSbKg+kdWfBOz389MfR0TOgYGxE0O6RGhMqOjkwRlQw76wmDCXT6uAXLlyqdliP5zl/jVmzBhZsWKFpuMiIu2gkA1KskHm4NiwHR7OesZEzoSBsRPbdGrTAzPFmYPjpOQkdRwZW79+vYwePVpee+01CQsLkzt37mg9JCLSAOoUR0WJ+PgY78dMMvazjjGRc2Fg7MTOpZyz6HGutAiwQYMGMmTIEPV1RESE1K9fX06cOKH1sIhIAwh+ExNFNmwQWbxYf5+QwKCYyBmx850T887vbdHjnhTymYf8NkQSryWm7/Mv6C9Tm06V9i/Y1xkGXfKmTJkiL7/8svTo0UN27dolVatWlW+++UaCUdWfiFyqFTO+Z2Cg7b8vEdkWZ4ydWMOSDcXXy1fcxPTKEez38/JTx9lqEWBA0QCJ6xUnKcNT1D22bbkIMLsz1q1bt5Y9e/ZIvXr1JDk5Wd544w21SI+IrA9d5cqWFQkKEgkJ0d9jm93miMhaGBg7Mfcc7hLRXL9yJHNwbNgObx6ujrMmBJ+YKW5VvpWs6LRC6vjWkXye+dQ9trH//d/et3paxeOWrcOCvNjYWBk2bJjaRmk3IrJNK+aAAJG4OJGUFP09ttmKmYisxU3nAEVbMVNXoEABuX79umrGQNmDwA/VKTIuxMNMMYJiW6QwYGYWQShmiBEMZxaXFCf1vq0nG7pvkED/QKvOWGeu0GH4gBDVIcqs9+LAgQNSoUKF9O2LFy9K0aJFrTBiItdOn8DMMIJgFIbJkWEKJy1NpF07kf37RY4dY0UIe8fzNzka5hi7AAR8bZ9rq6pPYKEdcoqRPmHtmeLMi/sqFq1o8nHDfmstAnxU2ToExyhbh/foUe9JxqD4/PnzqltemzZt1AK9PHnyWGX8RK4GOcVY7BYZaRwUA7aHDxepV09/HPN+iciSmErhIhDwYTa2c0BndW+roDjj4r79F/ebfNyw31qLAK1Vtm7NmjVqxnju3LlSq1YtOXTokAVGS0SGFssVTX+WTt/PVsxEZGkMjMnqMDuN6hMTNk2QNF2a0WPYnrh5opQuWNpqiwCtVbaua9eu8ttvv0mxYsVk//79UqNGDVm0aNFjjpKIMrdYRrqEKYb9bMVMRJbGwJisDrPTKMm28uhKafdDO5VTnHInRd1jG/unNJ1itVlsa5ata9Kkiapa0ahRI7l586Z0795devbsKTdu3HiMkRIRoCSbv7/IhAn6nOKMsD1xokjp0mzFTESWx8CYbJbnjAVu+y7uUwvtvCZ5qXukUZi78M1ey9YVL15czRx/+umnkiNHDlmwYIGMHTv2CUdN5LqwoG7qVJGVK/UL7TJWpcA29k+ZwoV3RGR5rEpBNoWFcFosAjRUpYCMi/CyW5XiUVDWDQHyTz/9JPnz53/i1yNyZSjJhgaUWIhngJliBMXsOucYeP4mR8PAmFyGFmXr8M8rPDxcevfuzUCZyIE739Hj4fmbHA0DY3Iptp6xnjZtmgwZMkTKly8vS5culcqVK1vtexER2Ruev8nRsI4xuWTZOltBGTdfX185evSo1K5dW80ev/322+LmZjrfmYisizPQRPQwXHxHZEUNGjSQ3bt3S8uWLeXOnTvSr18/6dSpk5pFISLb5yyj2kVQkEhIiP4e22wvTUQGDIyJrKxIkSLy888/y5QpU8TDw0OlVFSrVk0FzERkGwh+g4NFTmfq9XPmjH4/g2MiAgbGRDaAMm7INd60aZOUKlVKTp48KXfv3tV6WEQukz4RGorFsA8+ZtgXFqY/johcGwNjIhuqU6eOmiletmyZyjk2SMvcxYCILAY5xZlnijMHx0lJ+uOIyLUxMCaysaefflratGmTvv3nn39KxYoVZceOHZqOi8hZYaGdJY8jIufFwJhIY8OGDZNDhw5J/fr1VXk3B6igSORQUH3CkscRkfNiYEykscjISAkODpb79++rPOS2bdvK1atXtR4WkdNASTZfX5GsqiRiv5+f/jgicm0MjIk0huL3qFQxc+ZMyZUrl/zf//2fVKlSRbZu3ar10IicAuoUR0Tov84cHBu2w8NZz5iIGBgT2QU0/ECN423btkm5cuUkKSlJXnrpJdmyZYvWQyNyCu3bi0RFifj4GO/HTDL243EiIraEJrIzKSkpqjvehQsX5LfffhN3TmMRWQw739kWz9/kaBgYE9kh/LO8deuW5M2bV23j671796pyb0REjoLnb3I0TKUgstPUCkNQDIMHD1ZVK8aNGyep7EJARERkFQyMiewcAuGbN2+qJiCffPKJNG/eXKVZEBERkWUxMCayc8gxXrhwocyfP1/NIq9du1ZVrVi/fr3WQyMiInIqDIyJHESPHj3kjz/+kAoVKsj58+elSZMmMmrUKKZWEBERaRkYz5gxQ/z9/SV37txSu3btR7ayvXbtmrz77rvi7e2t6rSWL19efvnll8cdM5HLevHFF9W/t169eqkFetOnT2daBRERkYV4ZPcJS5YsUQuBZs+erYLi8PBwadasmRw5ckSKFi36wPF3796VV155RT0WFRUlPj4+cvLkSSlYsKClfgYil4J0innz5klQUJBa7V2iRAmth0REROSa5doQDNesWVO++uortY0FQX5+fjJw4EAZNmzYA8cjgP7888/l8OHDkjNnzscaJMu9ED3aqlWrVEOQTz/9VDw8sv2Zl4jI4nj+JqdOpcDs765du1RuY/oL5MihtuPi4kw+5+eff5a6deuqVIpixYpJxYoVZcKECQ/Ni7xz5476x5TxRkQPT1fq1q2bTJw4Uc0knz59WushEREROXdgfPnyZRXQIsDNCNtYDGTKiRMnVAoFnoe8YpSbmjp1qqrHmhWc3PEJ03DDjDQRZQ2pSbNmzZL8+fPL5s2bVdUK5vETERHZWVUKpFogv3jOnDlSvXp16dixo3z00UcqxSIrw4cPV5ddDLekpCRrD5PI4XXo0EHi4+OlWrVqcuXKFWnZsqUMHTpU7t27p/XQiIiInC8wLlKkiKqpmnkVPLaLFy9u8jmoRIEqFHiewQsvvKBmmJGaYQoqVyAXKeONiB6tbNmysnXrVpXzD8jvf+mll1RLaUtABlRsrEhkpP6eleKIiMhlA2NPT08167tu3TqjGWFsI4/YFLSxPX78uDrO4OjRoypgxusRkWXhg+WXX34py5YtU6lIlSpVkjx58jzx60ZHi/j7iwQFiYSE6O+xjf1EREQumUqBUm1z585VnbgOHTok/fr1kxs3bkjPnj3V41gAhFQIAzx+9epVCQ0NVQExVs5j8R0W4xGR9bRv31727NmjSioa4N9iVldqHgbBb3CwSOY1fWfO6PczOCYiImeQ7ZpOyBG+dOmSjBw5UqVDYJFPTExM+oK8U6dOqUoVBlg4t3r1ahk0aJCauUIdYwTJH374oWV/EiJ6ABrxGOCqDf79ooIF6pGXKVPGrNdAukRoqIipwo7Y5+YmEhYm0rYt2ldbcvRERER2XsdYC6yDSPTkcMUGKU+YNca/o2+++UaCMd37CMglRtrEo2zYIBIYaJmxEpFz4PmbHI3Vq1IQkX3AItjdu3dLvXr11MnqjTfeUClNt2/ffujzzp0z7/XNPY7+w8WMRET2hYExkQspWbKkxMbGpqcyzZw5UwXKx44dy/I53t7mvba5x5EeFzMSEdkfplIQuahff/1VLZZF4x5Um/njjz/EDQnDmWAWEwEbFtqZ+muBp/j6iiQkMMc4u4sZM7+fhrc/KgqLJzUZGpFF8fxNjoYzxkQu6tVXX1VVK5o2bSrz5s0zGRQDgt2ICP3XmQ8xbKPwBYNischiRsBiRqZVEBHZHgNjIheGKjGoGoPqMgaLFy+Ww4cPGx2H2UvMYvr4GD8fM8Wc3cyeTZseLHuXOThGs08cR0REdl6ujYicF9IpevTooZrvzJo1S7p27Zr+GIJflGRDwIaFdsgpbtiQM8XZxcWMRET2izPGRJTO19dXGjRooJr2IP/4rbfeUl8bIAhGSbbOnfX3DIqzj4sZiYjsFwNjIkqHVu1r1qyRMWPGqEY98+fPl1q1asmBAwe0HprTwCw7UlCySOlW+/389McREZFtMTAmIiPu7u6qs+W6deukePHicvDgQalZs6YsWLBA66E5BS5mJCKyXwyMicikwMBA2bt3r6pacevWLbly5YrWQ3IaXMxIRGSfWMeYiB4qLS1Nli5dKh06dFDpFZCamqpmlunJoCQbFzOSM+P5mxwNA2MiypZ//vlHGjZsKO+884707ds3y/rHREQ8f5OjYSoFEWULmoGgMQgC486dO6sTHxERkTNgYExE2fLee+/J559/Lh4eHrJkyRKpVq2axMfHaz0sIiKiJ8bAmIiyBXnG77//vmzcuFFKliwpf/31l9StW1e++uorcYDMLCIioiwxMCaix4JgePfu3dKmTRu5e/euDBw4UMaPH6/1sIiIiB4bA2MiemyFChWSFStWSHh4uOqa16tXL62HRERE9NgYGBPRE0FVitDQUDl69KjqnGeABiFMrSAiIkfCwJiILCJPnjzpX6PucZMmTaRdu3Zy9epVTcdFRERkLgbGRGRxKOHm6ekpP//8s1SpUkXi4uK0HhIREdEjMTAmIovr3bu3bNu2TcqWLStJSUmqIcjkyZNVFz0iIiJ7xcCYiKyiatWqsmvXLunUqZNqIf3hhx9Kq1at5PLly1oPjYiIyCQGxkRkNWgBu3jxYpkzZ47kzp1bfv31VzWTTEREZI88tB4AETl/1Yo+ffpI7dq15bffflOzxkRERPaIM8ZEZBOVKlVSHfMMTp8+rdIsLly4oOm4iIiIDBgYE5FmC/SWLFmiqlasX79e6+EQERExMCYibUybNk1efPFFOX/+vKp5PHr0aLVIj4iISCsMjB1UalqqxCbGSuS+SHWPbSJHgqD4jz/+kLfeekt1yBszZoy88sorcu7cOa2HRkRELspN5wA9W9EsoECBAnL9+nW1yt3VRR+KltCYUDmdfDp9n6+Xr0Q0j5D2L7TXdGxEj+N///ufvPPOO3Ljxg155plnZO3atSonmYgcG8/f5GgYGDtgUBy8NFh0Yvy/zU3c1H1UhygGx+SQDh8+LB07dlRNQLZv3y558+bVekh2D5knmzaJYJLd21ukYUMRd3etR0X0H56/ydEwMHYgSJfwj/A3minOHBxj5jghNEHcc/DsSI7n1q1bcunSJSlZsqTaRpCM7WLFimk9NLsTHS0SGorqHv/t8/UViYgQac/PxmQneP4mR8McYwey6dSmLINiwCxyUnKSOo7IEeXJkyc9KIZJkyZJhQoV5JdfftF0XPYYFAcHGwfFcOaMfj8eJyKi7GNg7EDOpZyz6HFE9uz+/fuyYsUKuXLlirRs2VKGDh0q9+7dE1eH9AnMFJu61mfYFxamP46IiLKHgbED8c7vbdHjiOyZh4eHbNy4UQYMGKC2P//8c3n55Zfl1KlT4sqQU5x5pjhzcJyUpD+OiIiyh4GxA2lYsqHKITYstMsM+/28/NRxRM4gd+7cMn36dImKilJ5inFxcaohyM8//yyuytxqdqx6R0SUfQyMHQgW1KEkG2QOjg3b4c3DufCOnM7rr78u8fHxUrNmTfn777+lQ4cOcvbsWXFFqD5hyeOIiOg/DIwdDEqxLQleIoXzFjbaj5lklmojZ1amTBnZvHmzDBo0SHXNK1GihLgilGRD9Qk30xeO1H4/P/1xRESUPR7ZPJ7soI7x0LVD5fLNy+n7vPN5y7Sm0xgUk9Pz9PRUQXFGO3fulJMnT6pZZVeAOsUoyYbqEwiCMy7CMwTL4eGsZ0xE9Dg4Y+yAzT0CigZIXK84SRmeou5rlKghHaI6qMeJXAlqo6IpSHBwsAwcOFBu374trgB1iqOiRHx8jPdjJhn7WceYiOjxsMGHAzX3KDu9rAqKV3RaITnc/vtMk6ZLk3Y/tJP9F/fLsYHHmGNMLgPl2z7++GOZPHmy2q5ataosXbpUypYtK66Ane/I3vH8TY7GZWeMEWjGJsZK5L5IdY9te4amHYnXEmVEwxFGQTFge3iD4ZJwLYHNPcil5MyZUz777DNZtWqVFC5cWHbv3i3VqlWTJUuWiCtAEBwYKNK5s/7ekYNiBPmxsSKRkfp71mEmIi24ZGCMlAO0Vg5aGCQh0SHqHtv2nIpgaNpRsWhFk48b9rO5B7miFi1ayJ49e6RBgwaSkpIinTp1krffflvu3r2r9dDIDOjU5+8vEhQkEhKiv8c2O/gRka3lcNU83cytlc8kn1H77TU4NjTtQLqEKYb9bO5BrsrX11c2bNggH330kbi5ucnp06dVkxCyb2xvTUT2xKVyjJEugZnhzEFxxlrAKHuWEJpgd3m6zDEmMt/atWtVI5AiRYqkt5dmkGx/kC6BmeGsOvmhygYWFCYkOHaaiCtjjjE5GpeaMUb+bVZBMehEJ0nJSXaZp4tgd2rTqbLy6EoVBMclxUnKnRR1j23sn9J0CoNiIhFp0qRJelAMvXv3ll69esnNmzc1HRcZY3trIrI3LjWFYm7+rb3m6aJOMZp4DPltiNT7tl76/tIFS7O5B1EW/vzzT1m0aJHg4ti2bdtU1YoKFSpoPSxie2siskMuFRibm39rz3m6CH7bPtdWzWojgMdYG5ZsyJlioixUqlRJ1q1bJyEhIXLw4EHVVnrGjBnSo0cPlYtM2mF7ayKyNy6ZY4yFdkibcKQcYyJ6MhcuXJCuXbvKmjVr1Da+njlzpuTLl0/roYmr5xhjoZ2pMxFzjB0fc4zJ0bhUjjGC3YjmEelBcEaG7fDm4QyKiZxQsWLFJCYmRsaPHy85cuSQ7777TpV5c4C5Aadvbw2ZJ+/Z3pqItOBSgXHGPF0fL+NeqpgpZp4ukXNDQDxixAiJjY0VPz8/1TWP6RTaYntrIrInLpVKkTmtgnm61sP3l+zd7du3JXfu3OnbcXFxalEeL/dqg+2tnRNTKcjRuGxgTNaDJimhMaFGpfEwI480Fs7Ikz06ceKEVK1aVYoWLaqqVuBrInpyPH+To3G5VAqyLkftLEiu7e+//1Yn7+PHj0udOnVU1QoHmDMgIiILY2BMFk2fwEyxqYofhn1hMWHqOCJ7Ur16ddmzZ4+0adNG7t69KwMGDJAOHTrItWvXtB4aERHZEANjshhH7ixIVKhQIVmxYoV88cUXkjNnTomKipJq1arJH3/8ofXQiIjIRhgYk8U4emdBIlSoCAsLky1btoi/v78kJCTI4sWLtR4WERHZc2CM/DucNLCiu3bt2rJjxw6znvfDDz+oE0+7du0e59uSnXOGzoJEgO54u3fvlmHDhsmkSZO0Hg4REdlrYLxkyRIZPHiwjBo1SuLj46Vy5crSrFkzuXjx4kOfl5iYKO+//740RA0eckooyYbqE5mbpxhgv5+XnzqOyN4VLFhQJk6cKLly5VLb9+/flzfffFOVdSMiIueU7cB42rRp0qdPH+nZs6e8+OKLMnv2bMmbN698++23WT4nNTVVunTpImPGjJEyZco86ZjJTrGzIDmz8PBw+f777+Wll16Szz//XNLS0rQeEhERaRkYY7X2rl27pEmTJv+9QI4cavthsyiffvqpqg/aq1cvs77PnTt3VO3DjDdyDOwsSM6qb9++0rFjRzVzPHToUGndurVcvnxZ62EREZEFeWTnYJwEMPtbrFgxo/3YPnz4sMnnbN68Wb755htVCslcuHyJ2WVyTAh+2z7Xlp3vyKmgOUFkZKQ0atRI3nvvPfnll1+kSpUqah9TxIiInINVq1KkpKRI165dZe7cuVKkSBGznzd8+HDVJcdwS0pKsuYwyQoQBAf6B0rngM7qnkExOQMsHsbMMRYcly9fXs6cOSNBQUEPTSUjIiInnTFGcOvu7i4XLlww2o/t4sWLP3D8X3/9pRbd4ZKjgSEvz8PDQ44cOSLPPvvsA8/DYhfDghciIntTqVIllVbWr18/iY6OVt3yiIjIxWaMPT09VYeodevWGQW62K5bt+4Dxz///POyb98+lUZhuKGzFGZY8LWfn59lfgoiG0HXvtjEWIncF6nu2cXPdeXLl08WLVoke/fuVQuRDTCLTERELjBjDCjV1r17d6lRo4bUqlVLrdS+ceOGqlIB3bp1Ex8fH5UnjDrHFStWfKAEEmTeT2Tvog9Fy5DfhkjitcT0ff4F/WVq06lcVOjCqRVly5Y1WlPRuHFjGTFihHz88cfqChsRETlxjjFWZU+ZMkVGjhypFp5g5jcmJiZ9Qd6pU6fk3Dl2NiPnC4qDlwZLQNEAiesVJynDU9Q9trEfjxOtWrVKVe8ZPXq0NG3aVM6fP6/1kIiIKBvcdDqdTuwcyrUVKFBALcTDynAiW0K6RNnpZVUQvKLTCsnh9t/nyTRdmrT7oZ3sv7hfjg08xkWGJN99953KPcaVNJSpRO3jjCUuiVwJz9/kaKxalYLIGaDsHNInRjQcYRQUA7aHNxguCdcS1HFEqMSzc+dOCQgIUB1BMXOMtArUPyYiIvvGwJjoEVCLGSoWNZ0Xb9hvOI4IC4+3b9+uSrvhotz48eNl+fLlWg+LiIgsvfiOyNWgQQkgXaKO74NlubA/43FEkCdPHvn6669VFZ7ffvtNgoODxZWlpops2iSCJSje3iLoicK1iURkbzhjTPQI6NqH6hMTNk1QOcUZYXvi5olSumBpdRxRZp06dVINQFDBApBrOWnSJLl37564iuhoEX9/kaAgkZAQ/T22sZ+IyJ4wMCZ6BCyoQ0m2lUdXqoV2cUlxknInRd1jG/unNJ3ChXdkFqRXoLtnYGCgquLj7BD8YrL89Gnj/Sj3jP0MjonInrAqBdET1DHGTDGCYtYxJnNFRUVJr1691N+1p59+WhYuXGjUHdTZ0icwM5w5KDbAJLqvr0hCAtMqnBXP3+RoGBgTZbN0G6pPYKEdcoqRPsGZYsquEydOqJrwqF5haJyEpkjoLupMYmP1aROPsmGDSGCgLUZEtsbzNzkaplIQZQOC4ED/QOkc0FndMyimx1GmTBnZsmWLhIWFqe1p06ZJw4YNJSkpSZyJub2e2BOKiOwFA2MiIg1gdviLL76QFStWSMGCBeX06dOSO3ducSaoPmHJ44iIrI3l2oiINNS2bVvZs2ePagbyzDPPpO9HQxAPD8f+E42SbMghxkI7U0l7hhxjHEdEZA84Y0xEpLFSpUpJzZo107cXLVoktWvXluPHj4sjw4K6iAj91/9Wq0tn2A4P58I7IrIfDIyJiOzI3bt3ZeTIkRIfHy/VqlWTpUuXiiNr3x6VOER8fIz3Y6YY+/E4EZG9YFUKIiI7g3zjzp07y+bNm9X222+/rfKR0U3PUbHznWvi+ZscDQNjIiI7hBzjUaNGqTJu+DNdqVIlNXv83HPPaT00IrPx/E2OhqkURER2CAvvxo8fLzExMWpR3p9//qnykLFIj4iIrMOxlzwTETm5pk2bqqoVXbp0kTp16kjRokW1HhIRkdNiYExEZOdKlCgha9euVSkVBgkJCXLr1i158cUXNR0bEZEzYSoFEZEDcHd3T69rjMoVaCmN1IoFCxY89mI4tGyOjNTfY5uIyNUxMCYicjA3btxQ3fJu3rwpPXv2lO7du8s///xj9vOjo0X8/UWCgkRCQvT32MZ+IiJXxsCYiMjBPP3002pR3rhx4yRHjhyqIQhmj/ft2/fI5yL4DQ5GSTjj/ehOh/0MjonIlbFcGxG5jNS0VNl0apOcSzkn3vm9pWHJhuKew7GL6W7cuFHVPD579qzkzp1bvvzyS+ndu7e4ZW4192/6BGaGMwfFmVs0JySwxjBZBs/f5Gg4Y0xELiH6ULT4R/hL0MIgCYkOUffYxn5H9tJLL6mqFa+++qrcvn1bZs+erWogm4IGG1kFxYBpkqQk/XFERK6IgTEROT0Ev8FLg+V0snFUeCb5jNrv6MEx6hyvXLlSpkyZIkuWLJGcOXOaPA5d58xh7nFERM6GgTEROX36RGhMqOjkwawxw76wmDB1nCNDrvGQIUOkbNmy6fuQgzxz5sz0Mm9oxWwOc48jInI2DIyJyKkhpzjzTHHm4DgpOUkd50zi4+Nl5MiR8u6770qHDh1UjmfDhvocYhPpxwr2+/mJOo6IyBUxMCayc5jJjE2Mlch9kere0Wc2bQ0L7Sx5nKOoWrWqTJ06VaVVREVFSbVq1WT37p0SEaF/PHNwbNgOD+fCOyJyXQyMieyYsy4YsyVUn7DkcY4CVSkGDRokmzdvFn9/fzlx4oTUq1dPkpIi5McfdeLjY3w8ZpKjokTat9dqxERE2mO5Nhcv9UT2v2Asc26sm+in9qI6REn7FxjFmPPvFh8msNDOVJ4x3k9fL19JCE1w2n/P165dk169ekn0v0WKQ0JCZNGi71X1CSy0Q04x0ic4U0zOev4mMhdnjLMRpJSdXtZo5g7bnLkja3CVBWO2gGA3onmE0YcKA8N2ePNwpw2KAV3ykE4xffp08fT0lCZNmqggODBQpHNn/T2DYiIiBsbZmrkLKBogcb3iJGV4irrHtjOUeiL746oLxqwFM+uYYffxMs4fwEyxq8y8I7ViwIABcvjwYenRo0f6/tOnT0taWpqmYyMishdMpXgEzMhhZhhB8IpOKySH23+fJdJ0adLuh3ay/+J+OTbwmFPPOJFtYaEdrkw8yuL2i6VzQGebjMkZMB3K2JUrV6RKlSpSuXJlWbhwoRQuXFjrIZGTYSoFORrOGD8CTqKJ1xJlRMMRRkExYHt4g+GScC2BM3dkUa66YMzaEAQH+geqDxO4d+WgGHbs2CGXLl2SVatWqQAZC/WIiFwZA2MzSzhVLFrR5OOG/c5W6om0hZlMXObPnBNrgP1+Xn7qOKLHhTbS27dvl/Lly6uUisDAQJk4cSJTK4jIZTEwNnNGDukSphj2c+aOLIkLxshWkEaxa9cuefPNNyU1NVVGjBghLVq0kIsXL2o9NCIim2Ng/AiYkfMv6C8TNk1QOcUZYXvi5olSumBpztyRxXHBGNlKvnz5ZNGiRfLNN99Injx5ZPXq1fLJJ59oPSwiIpvj4rtsVKVoVb6VyilG+gRmihEUrzy6kkEKWRUXjJEtHThwQIYOHSrff/+9KvNG5Mjnb6LsYmCcjeB4yG9D1EI8A8wUT2k6hUExETktnCImT54s3bt3l+LFi2s9HHIw9nD+JsoOBsbZwJk7InI1c+bMkbfffluKFSsm//vf/1RzECJHO38TmYs5xtnAUk9E5GpeeuklCQgIkAsXLkjTpk1V7vH9+/e1HhYRkVUwMCYioiw9//zzqqRb3759VVrFuHHjpHHjxnLmzBmth0ZEZHEMjImI6KFQqeLrr7+WyMhIVcFi48aNqiHI2rVrtR4aEZFFMTAmIiKzdOrUSeLj41VQfPXqVcmZM6fWQyIisigPy74cERE5s3LlyklcXJzExsbKyy+/nL7/3r17DJSJyOFxxpiIiLIld+7c0rx58/TtI0eOSNmyZWXlypWajouI6EkxMCYioicyadIkOXXqlLRu3VqGDBkid+/eFWeRmioSGysSGam/xzYROS8GxkRE9ESwMC8sLEx9PW3aNGnYsKEkJv7XDMlRRUeL+PuLBAWJhITo77GN/UTknBgYE5FRE5vYxFiJ3Bep7rFN9Cienp7yxRdfyIoVK1Qb6R07dkjVqlVl+fLl4qgQ/AYHi5w+bbwfVeqwn8ExkXNi5zsiSm97HhoTKqeT/4sEfL18JaJ5BNuek9lOnjypqlds27ZNbf/000/Spk0bcSRIl8DMcOag2MDNTcTXVyQhQcSdfZ4eiudvcjScMSYiFRQHLw02CorhTPIZtR+PE5mjVKlSqs7xBx98IPXr15dXX31VHM2mTVkHxYDppKQk/XFE5FwYGBO5OKRLYKZYJw9ePDLsC4sJY1oFmQ1l2yZPnizr169PL+GGBXkxMTHiCM6ds+xxROQ4GBgTubhNpzY9MFOcOThOSk5SxxFlN/fYYPjw4Wr2uH///nL79m2xZ97elj2OiBwHA2MiF3cu5ZxFjyPKDEtZ0FYaZs2aJXXq1JGjR4+KvWrYUJ9DjFxiU7Dfz09/HBE5FwbGRC7OO7+3RY8jyszNzU3GjRunUimeeeYZ2bt3r1SvXl0WL14s9ggL6iIi9F9nDo4N2+HhXHhH5IwYGBO5uIYlG6rqE25ienoM+/28/NRxRE+iWbNmsmfPHgkMDJR//vlHunTpIn369JGbN2+KvWnfXiQqSsTHx3g/ZpKxH48TkfNhYEzk4txzuKuSbJA5ODZshzcPV8cRPakSJUrI2rVrZeTIkWomecmSJXLOTlexIfhFn5ING0QwuY17lGhjUEzkvFjHmIiyrGOMmWIExaxjTNaAqhX4+96uXTuth0JWwvM3ucSM8YwZM8Tf319y584ttWvXVl2OsjJ37lzVHvTpp59WtyZNmjz0eCLSBoLfxNBE2dB9gyxuv1jdJ4QmMCgmq2nUqJFRULxu3Trp2bOn3LhxQ9NxEZHrynZgjMtegwcPllGjRkl8fLxUrlxZ5Y1dvHjR5PGxsbHSuXNn2bBhg8TFxYmfn580bdpUzqCvJhHZFaRLBPoHSueAzuqe6RNkKyjh1r17d1mwYIG8WPlFmRw9mW3Jicj+UykwQ1yzZk356quv1HZaWpoKdgcOHCjDhg175PNTU1PVzDGe361bN7O+Jy/FEBE5v7HfjZUxA8dI6vVUEQ8ReVWkVKNSMq3ZNF65cFA8f5NTzxijc9GuXbtUOkT6C+TIobYxG2wOrD6+d++eFCpUKMtj7ty5o/4xZbwRkX3CjB5m9iL3RXKGj54ox33UiVHS+LPGUiewjsh9Efk/kfs/3pfXv3udbcmJyP4C48uXL6sZ32LFihntx/b58+fNeo0PP/xQrUrOGFxnNnHiRPUJ03DDjDQR2R8EK/4R/hK0MEhCokPUPbYZxFB24MPUkN+GSKvyreTXvr/KlnVb5LPPPhN3d3c5s/WMPDX/KRm0fBA/dBGRc5VrmzRpkvzwww+yfPlytXAvK2gdissuhltSUpIth0lEZkDwG7w0+IF20meSz6j9DI7JXGg3nngtUUY0HCE53HKoK5FDhw6VjRs3qomR+nXry6m7p9iWnIisDllcZitSpIj6BH/hwgWj/dguXrz4Q587ZcoUFRijfmWlSpUeemyuXLnUjYjsE2buUNpNJw8uUcA+1D8OiwmTts+15QI+MrvdeMWiFY3216tXT3bv3i13dHfEZ4aPOu7vv/9WgTOuJhIRaTpj7Onpqdp4oqSOARbfYbtu3bpZPm/y5MkyduxY1Q60Ro0aTzZiItIcZu4yzxRnDo6TkpM4w0fZaje+/+L+Bx4rXLiwnLx1Un1dPF9xVbmiWrVqsnPnTpuPk4icX7ZTKVCqDbWJFy5cKIcOHZJ+/fqpmpOoPQmoNIFUCAPkiX3yySfy7bffqtrHyEXGDe1AicixZ/gsdRy5NrQb9y/oLxM2TZA0XZrRY9ieuHmilC5YWsrmLCv79u2TEydOqNnkL7/8UhygRxUROXNg3LFjR5UWgXaeVapUUX3vMRNsWJB36tQpo/aes2bNUtUsgoODxdvbO/2G1yAix57hs9Rx5NqQbjO16VRZeXSltPuhncQlxUnKnRR1j23sn9J0ivj5+qn6+a+99pqqbhQaGiqvv/66Sq8gIrIEtoQmosfKMUb1CSy0M5VnjBxjXy9f1TmPOcZkLizYRHUKLMQzwEwxguKMdYxx2kIH1iFDhqiJl1KlSqnmU6izT/aF529yNAyMieiJqlJAxuAYQTFEdYhiUwZ6rA9dyE1HGg6uOCDNIqsPV6irj6uYf/31l5QrV04OHjwoHh7ZWlNOVsbzNzkaBsZE9ETBMapTZFyI5+flJ+HNwxkUk83OD/3791fdVzljbH94/iZHw8CYiGw2w0dkC5GRkVKyZEmpX7++1kNxeTx/k6PhNScieiIIggP9A7UeBpGyf/9+eeutt9TivHHjxqlGIah7TERkDv61ICIip4GFeKhUkZqaqkqHtmzZUi5duqT1sIjIQTAwJiIip5E/f3757rvvZN68eZInTx5VThSlRX///Xeth0ZEDoCBMRERORU3Nzfp1auX7NixQ1544QU5e/asNGrUSCZMmKD10IjIzjEwJiIip1SxYkX5448/pEePHpKWlib379/XekhEZOe4+I6IiJzWU089JfPnz5c33nhDmjVrlr4fjUE8PT01HRsR2R/OGBMRkdNr0aKFuLvrywjevn1b6tatK6NGjVKL9IiIDDhjTETkhFhfOmvLli2T+Ph4dcOivMWLF0uJEiW0HhYR2QEGxkRELtCR0NfLVyKaR7AjoYh06dJFLdB7++23VWCMqhWoZJEx1YKIXBNTKYiInCwoDl4abBQUw5nkM2o/HieRkJAQ2bVrlwqKUee4efPmqu4xF+gRuTYGxkRETpQ+gZlinegeeMywLywmTB1HIuXLl5e4uDjp37+/2p40aZIMHjxY62ERkYYYGBMROQnkFGeeKc4cHCclJ6njSC937twyY8YMWbp0qZQuXVref/99rYdERBpiYExE5CSw0M6Sx7kSlHM7cuSIlCxZMn3fypUr5d69e5qOi4hsi4ExEZGTQPUJSx7nanLmzJn+9apVq6R169bSsGFDSUxM1HRcRGQ7DIyJiJwESrKh+oSbuJl8HPv9vPzUcfRwOp1OChYsKNu3b5eqVavKihUrtB4SEdkAA2MnggU1sYmxErkvUt1zgQ2Ra0GdYpRkg8zBsWE7vHk46xmboVWrVrJ7926pXbu2XLt2TV577TUJCwuTO3fuaD00IrIiBsZOAiWY/CP8JWhhkIREh6h7bLM0E5FrQZ3iqA5R4uPlY7QfM8nYzzrG5vP395eNGzfKkCFD1HZERITUr19fTpw4ofXQiMhK3HS4XmTnkpOTpUCBAnL9+nXx8vLSejh2W7c0c4kmwwwRT4ZEroed7ywLC/G6d+8uV69elcjISOnUqZPWQ3IIPH+To2Fg7AQnP8wMZ1WiCcExZooSQhN4UiQiegJJSUkSFRUlgwYN0nooDoPnb3I0TKVwcKxbSkRkG35+fkZB8YULF6RFixZy7NgxTcdFRJbDwNjBsW4pEZE2sBjv119/lWrVqqn0CiJyfAyMHRzrlhIRaWPq1KkSGBgo//zzj4SEhEjfvn3l1q1bWg+LiJ4AA2MHx7qlRETaKFGihKxdu1ZGjhwpbm5uMnfuXKlVq5YcOnRI66ER0WNiYOzgWLeUiEg77u7uMmbMGFmzZo0UK1ZM9u/fLzVq1JDff/9d66ER0WNgYOwEWLeUiEhbjRs3lj179qh7Hx8flXdMRI6H5dqcCOuWEhFpKzU1VVWrQJoF4BSbkJAgZcqUEVfE8zc5Gs4YOxEEwYH+gdI5oLO6Z1BMRGT71ApDUGzollehQgWZN2+eCpKJyL4xMCYiIrICBMIbNmyQ27dvS58+feTNN9+UlJQUrYdFRA/BwJiIiMgKUKli+fLlMnHiRDWTvHjxYrUwb+/evVoPjYiywMCYiIjISnLkyCHDhg1TVSp8fX3l6NGjUrt2bZk9ezZTK4jsEANjIiIiK6tfv76qWtGqVSu5c+eODBw4UAXJRGRfPLQeABERkSsoXLiw/Pzzz/LFF1+omeTnnntO6yERUSYMjImIiGyYdzx48GCjfX/++adKtRgwYIB6nIi0w8CYiJwea3yTvbp165Z07NhRDh8+rCpYfPPNN/L0009rPSwil8UcYyJyatGHosU/wl+CFgZJSHSIusc29hNpLXfu3NK/f3/x9PRUFSzQMW/79u1aD4vIZTEwJiKnheA3eGmwnE4+bbT/TPIZtZ/BMWkNqRNYiLd161bVHS8xMVEaNGgg06ZNY9UKIg0wMCYip02fCI0JFZ08GFwY9oXFhKnjiLRWvXp1iY+PlzfeeEPu378vQ4YMkTZt2rAhCJGNMTAmIqeEnOLMM8WZg+Ok5CR1HJE9KFCggCxZskRmzZoluXLlkhs3bkjevHm1HhaRS+HiOyJySlhoZ8njiGyVWvHOO+9InTp1pFixYqpjHty9e1c8PDxUmTcish7+CyMip4TqE5Y8jsiWqlSpIt7e//1uhoWFScuWLeXSpUuajovI2TEwJqLHgtzc2MRYidwXqe7tLVcXJdl8vXzFTUzXhcV+Py8/dRyRPTt58qTMnz9fYmJiVMC8ceNGrYdE5LQYGBORU5ZAQ53iiOYR6uvMwbFhO7x5OOsZk90rVaqU7NixQ55//nk5e/asBAUFybhx4yQ11b4+jBI5AwbGROS0JdDav9BeojpEiY+Xj9F+zCRjPx4ncgQBAQGyc+dO6d69u6Slpcknn3wizZs3lwsXLmg9NCKn4qZzgEKJycnJarXu9evXxcvLS+vhELkspEtgZjirag+YiUXQmRCaYFczsex8R85k4cKFqinIzZs35cUXX1QtpQ2L9OwNz9/kaFiVgoisUgIt0D9Q7AWCYHsaD9GTwKxxzZo1VStppFTYa1BM5IgYGBOR2VgCjcg+YKZ49+7dqoSbwZYtW6R06dJSokQJTcdG5MiYY0xEZmMJNCL7kTEoPn36tLRt21YqV64sq1ev1nRcRI6MgTERmY0l0Ijs0507d8TX11cuX76sFuUNHz5ctZYmouxhYExEZmMJNCL79Oyzz8q2bdukX79+anvSpEmqrBtmkonIfAyMiShbWAKNyD7lzp1bZs6cKUuWLJH8+fPL5s2bVUOQX375ReuhETkMlmsjosfCEmhE9uuvv/5SVSt27dolvXv3lrlz52oyDp6/ydEwMCYiInLSvONp06ZJaGio5M2bV5Mx8PxNjoapFPREM4axibESuS9S3WObiIjsQ65cudQiPENQjI55nTp1khUrVmg9NCLnCoxnzJgh/v7+Kp+pdu3aqof7w/z444+qxzuOR1tL5js5PrT9RQe0oIVBEhIdou6xbU/tgImI6D8LFixQ+cevvfaahIWFqRllInrCwBj/qAYPHiyjRo2S+Ph4VTOxWbNmcvHiRZPHb926VTp37iy9evVSxcjbtWunbvv378/utyY7geA3eGnwAx3QziSfUfsZHBMR2Z8333xThgwZor6OiIiQ+vXrq1xkInqCHGPMEKMV5VdffZV+acbPz08GDhwow4YNe+B4JP/fuHFDVq5cmb6vTp06aqXs7NmzzfqezFGyH0iXwMxwVm2BUbIL1QkSQhO4EIuIyA7hfIy20levXlXn1Hnz5skbb7xhle/F8zc59Yzx3bt31QrXJk2a/PcCOXKo7bi4OJPPwf6MxwNmmLM6HnB5B/+YMt7IPqAKQVZBMehEJ0nJSeo4IiKyP61atZI9e/aoGWOcXzt06CDjx4/XelhEjhcYo6NOamqqFCtWzGg/ts+fP2/yOdifneNh4sSJ6hOm4YYZabIPKM1lyeOIiMj2cF7dsGGDutKL9T8tWrTQekhEdsEuq1JgFS0uuxhuSUlJWg+J/oV6tZY8joiItJEzZ041EXX8+HGpWrWq1sMhsgse2Tm4SJEi4u7uLhcuXDDaj+3ixYubfA72Z+d4Q4kZ3Mj+oIkDcoix0A5pE1nlGOM4IiKyfz4+xl0siVxZtmaMPT09pXr16rJu3br0fVh8h+26deuafA72Zzwe1qxZk+XxZN+woC6ieUR6EJyRYTu8eTgX3hEREZHzp1KgVBtaSy5cuFAOHTok/fr1U1UnevbsqR7v1q2bSoUwQMedmJgYmTp1qhw+fFhGjx4tO3fulAEDBlj2JyGbaf9Ce4nqECU+XsazDJgpxn48TkREROTUqRSG8muXLl2SkSNHqgV0KLuGwNewwO7UqVOqUoVBvXr1ZPHixfLxxx/LiBEjpFy5cqrrTsWKFS37k5BNIfht+1xbVX0CC+2QU4z0Cc4UExERkcvUMdYC6yASERE5Hp6/ydHYZVUKIiIiIiJbY2BMRERERMTAmIiIiIhIj4ExEREREREDYyIiIiIiPQbGREREREQMjImIiIiI9BgYExERERExMCYiIiIiesyW0FowNOdDBx0iIiJyDIbztgM02SVynMA4JSVF3fv5+Wk9FCIiInqM8zhaQxPZOzedA3yMS0tLk7Nnz0r+/PnFzc3Nop9kEWwnJSWxh7sV8X22Hb7XtsH32Tb4Pjv++4wQA0FxiRIlJEcOZm+S/XOIGWP8Y/L19bXa6+MPAf/oWh/fZ9vhe20bfJ9tg++zY7/PnCkmR8KPb0REREREDIyJiIiIiPRcOjDOlSuXjBo1St2T9fB9th2+17bB99k2+D7bBt9nIgdbfEdEREREZG0uPWNMRERERGTAwJiIiIiIiIExEREREZEeA2MiIiIiIlcIjGfMmCH+/v6SO3duqV27tuzYseOhx//444/y/PPPq+MDAgLkl19+sdlYXeV9njt3rjRs2FCefvppdWvSpMkj/7/Q4/9OG/zwww+qc2S7du2sPkZXfJ+vXbsm7777rnh7e6vV/eXLl+ffDyu8z+Hh4fLcc89Jnjx5VLe2QYMGye3bt202Xke0ceNGad26teo+h78BK1aseORzYmNjpVq1aup3uWzZsrJgwQKbjJVIczon9sMPP+g8PT113377re7AgQO6Pn366AoWLKi7cOGCyeO3bNmic3d3102ePFl38OBB3ccff6zLmTOnbt++fTYfuzO/zyEhIboZM2bodu/erTt06JCuR48eugIFCuhOnz5t87E7+3ttkJCQoPPx8dE1bNhQ17ZtW5uN11Xe5zt37uhq1Kiha9GihW7z5s3q/Y6NjdXt2bPH5mN35vf5+++/1+XKlUvd4z1evXq1ztvbWzdo0CCbj92R/PLLL7qPPvpIFx0djSpUuuXLlz/0+BMnTujy5s2rGzx4sDoXTp8+XZ0bY2JibDZmIq04dWBcq1Yt3bvvvpu+nZqaqitRooRu4sSJJo/v0KGDrmXLlkb7ateurXv77betPlZXep8zu3//vi5//vy6hQsXWnGUrvte4/2tV6+ebt68ebru3bszMLbC+zxr1ixdmTJldHfv3rXhKF3vfcaxjRo1MtqH4K1+/fpWH6uzMCcwHjp0qK5ChQpG+zp27Khr1qyZlUdHpD2nTaW4e/eu7Nq1S12mN8iRI4fajouLM/kc7M94PDRr1izL4+nx3ufMbt68Kffu3ZNChQpZcaSu+15/+umnUrRoUenVq5eNRup67/PPP/8sdevWVakUxYoVk4oVK8qECRMkNTXVhiN3/ve5Xr166jmGdIsTJ06odJUWLVrYbNyugOdCcmUe4qQuX76sTko4SWWE7cOHD5t8zvnz500ej/1kufc5sw8//FDlvmX+Q0xP/l5v3rxZvvnmG9mzZ4+NRuma7zMCtPXr10uXLl1UoHb8+HHp37+/+sCHjmJkmfc5JCREPa9Bgwa42in379+Xd955R0aMGGGjUbuGrM6FycnJcuvWLZXfTeSsnHbGmBzDpEmT1KKw5cuXq8U3ZDkpKSnStWtXtdixSJEiWg/HqaWlpalZ+Tlz5kj16tWlY8eO8tFHH8ns2bO1HppTwYIwzMTPnDlT4uPjJTo6WlatWiVjx47VemhE5CScdsYYgYC7u7tcuHDBaD+2ixcvbvI52J+d4+nx3meDKVOmqMB47dq1UqlSJSuP1PXe67/++ksSExPVavSMARx4eHjIkSNH5Nlnn7XByJ3/dxqVKHLmzKmeZ/DCCy+omTekDHh6elp93K7wPn/yySfqw17v3r3VNioH3bhxQ/r27as+iCAVg55cVudCLy8vzhaT03PavyI4EWHmZt26dUZBAbaRC2gK9mc8HtasWZPl8fR47zNMnjxZzfLExMRIjRo1bDRa13qvUXZw3759Ko3CcGvTpo0EBQWpr1HqiizzO12/fn2VPmH44AFHjx5VATODYsu9z1iPkDn4NXwY0a8rI0vguZBcms7JSwGhtM+CBQtUyZm+ffuqUkDnz59Xj3ft2lU3bNgwo3JtHh4euilTpqgyYqNGjWK5Niu8z5MmTVIlmqKionTnzp1Lv6WkpGj4Uzjne50Zq1JY530+deqUqqwyYMAA3ZEjR3QrV67UFS1aVDdu3DgNfwrne5/xNxnvc2RkpCop9ttvv+meffZZVVGIsoa/rSiPiRtO+9OmTVNfnzx5Uj2O9xjvdeZybR988IE6F6K8Jsu1katw6sAYUH+xZMmSKhBDaaBt27alP/byyy+rQCGjpUuX6sqXL6+OR7maVatWaTBq536fS5Uqpf44Z77hpEeW/53OiIGx9d7nrVu3qvKOCPRQum38+PGqVB5Z7n2+d++ebvTo0SoYzp07t87Pz0/Xv39/3d9//63R6B3Dhg0bTP7NNby3uMd7nfk5VapUUf9f8Ps8f/58jUZPZFtu+I/Ws9ZERERERFpz2hxjIiIiIqLsYGBMRERERMTAmIiIiIhIj4ExEREREREDYyIiIiIiPQbGREREREQMjImIiIiI9BgYExERERExMCYiIiIi0mNgTERERETEwJiIiIiISI+BMREREREJifw/GTaQAqMci1wAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_dataset():\n",
    "    plt.scatter(\n",
    "        train_features[np.where(train_labels[:, 0] == 0), 0],\n",
    "        train_features[np.where(train_labels[:, 0] == 0), 1],\n",
    "        marker=\"o\",\n",
    "        color=\"b\",\n",
    "        label=\"Label 0 train\",\n",
    "    )\n",
    "    plt.scatter(\n",
    "        train_features[np.where(train_labels[:, 0] == 1), 0],\n",
    "        train_features[np.where(train_labels[:, 0] == 1), 1],\n",
    "        marker=\"o\",\n",
    "        color=\"g\",\n",
    "        label=\"Label 1 train\",\n",
    "    )\n",
    "\n",
    "    plt.scatter(\n",
    "        test_features[np.where(test_labels[:, 0] == 0), 0],\n",
    "        test_features[np.where(test_labels[:, 0] == 0), 1],\n",
    "        marker=\"o\",\n",
    "        facecolors=\"w\",\n",
    "        edgecolors=\"b\",\n",
    "        label=\"Label 0 test\",\n",
    "    )\n",
    "    plt.scatter(\n",
    "        test_features[np.where(test_labels[:, 0] == 1), 0],\n",
    "        test_features[np.where(test_labels[:, 0] == 1), 1],\n",
    "        marker=\"o\",\n",
    "        facecolors=\"w\",\n",
    "        edgecolors=\"g\",\n",
    "        label=\"Label 1 test\",\n",
    "    )\n",
    "\n",
    "    plt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\", borderaxespad=0.0)\n",
    "    plt.plot([1, 0], [0, 1], \"--\", color=\"black\")\n",
    "\n",
    "\n",
    "plot_dataset()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "regulation-depression",
   "metadata": {},
   "source": [
    "On the plot above we see:\n",
    "\n",
    "* Solid <span style=\"color:blue\">blue</span> dots are the samples from the training dataset labeled as `0`\n",
    "* Empty <span style=\"color:blue\">blue</span> dots are the samples from the test dataset labeled as `0`\n",
    "* Solid <span style=\"color:green\">green</span> dots are the samples from the training dataset labeled as `1`\n",
    "* Empty <span style=\"color:green\">green</span> dots are the samples from the test dataset labeled as `1`\n",
    "\n",
    "We'll train our model using solid dots and verify it using empty dots."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "egyptian-campaign",
   "metadata": {},
   "source": [
    "## 2. Train a model and save it\n",
    "\n",
    "We'll train our model in two steps. On the first step we train our model in `20` iterations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "brief-lending",
   "metadata": {},
   "outputs": [],
   "source": [
    "maxiter = 20"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "crude-franklin",
   "metadata": {},
   "source": [
    "Create an empty array for callback to store values of the objective function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "integrated-palestinian",
   "metadata": {},
   "outputs": [],
   "source": [
    "objective_values = []"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "legendary-sherman",
   "metadata": {},
   "source": [
    "We re-use a callback function from the Neural Network Classifier & Regressor tutorial to plot iteration versus objective function value with some minor tweaks to plot objective values at each step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "periodic-apparel",
   "metadata": {},
   "outputs": [],
   "source": [
    "# callback function that draws a live plot when the .fit() method is called\n",
    "def callback_graph(_, objective_value):\n",
    "    clear_output(wait=True)\n",
    "    objective_values.append(objective_value)\n",
    "\n",
    "    plt.title(\"Objective function value against iteration\")\n",
    "    plt.xlabel(\"Iteration\")\n",
    "    plt.ylabel(\"Objective function value\")\n",
    "\n",
    "    stage1_len = np.min((len(objective_values), maxiter))\n",
    "    stage1_x = np.linspace(1, stage1_len, stage1_len)\n",
    "    stage1_y = objective_values[:stage1_len]\n",
    "\n",
    "    stage2_len = np.max((0, len(objective_values) - maxiter))\n",
    "    stage2_x = np.linspace(maxiter, maxiter + stage2_len - 1, stage2_len)\n",
    "    stage2_y = objective_values[maxiter : maxiter + stage2_len]\n",
    "\n",
    "    plt.plot(stage1_x, stage1_y, color=\"orange\")\n",
    "    plt.plot(stage2_x, stage2_y, color=\"purple\")\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (12, 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "institutional-cyprus",
   "metadata": {},
   "source": [
    "As mentioned above we train a `VQC` model and set `COBYLA` as an optimizer with a chosen value of the `maxiter` parameter. Then we evaluate performance of the model to see how well it was trained. Then we save this model for a file. On the second step we load this model and will continue to work with it.\n",
    "\n",
    "Here, we manually construct an ansatz to fix an initial point where to start optimization from."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "electronic-impact",
   "metadata": {},
   "outputs": [],
   "source": [
    "original_optimizer = COBYLA(maxiter=maxiter)\n",
    "\n",
    "ansatz = real_amplitudes(num_features)\n",
    "initial_point = np.asarray([0.5] * ansatz.num_parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "separated-classroom",
   "metadata": {},
   "source": [
    "We create a model and set a sampler to the first sampler we created earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "revolutionary-freeze",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No gradient function provided, creating a gradient function. If your Sampler requires transpilation, please provide a pass manager.\n"
     ]
    }
   ],
   "source": [
    "original_classifier = VQC(\n",
    "    ansatz=ansatz, optimizer=original_optimizer, callback=callback_graph, sampler=sampler1\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "minute-mexican",
   "metadata": {},
   "source": [
    "Now it is time to train the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "suited-appointment",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<qiskit_machine_learning.algorithms.classifiers.vqc.VQC at 0x1f3c636c4d0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_classifier.fit(train_features, train_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "revised-torture",
   "metadata": {},
   "source": [
    "Let's see how well our model performs after the first step of training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "greek-memphis",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score 0.8\n",
      "Test score  0.8\n"
     ]
    }
   ],
   "source": [
    "print(\"Train score\", original_classifier.score(train_features, train_labels))\n",
    "print(\"Test score \", original_classifier.score(test_features, test_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rental-moses",
   "metadata": {},
   "source": [
    "Next, we save the model. You may choose any file name you want. Please note that the `save` method does not append an extension if it is not specified in the file name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "broadband-interview",
   "metadata": {},
   "outputs": [],
   "source": [
    "original_classifier.to_dill(\"vqc_classifier.model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sitting-thread",
   "metadata": {},
   "source": [
    "## 3. Load a model and continue training\n",
    "\n",
    "To load a model a user have to call a class method `load` of the corresponding model class. In our case it is `VQC`. We pass the same file name we used in the previous section where we saved our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "steady-europe",
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded_classifier = VQC.from_dill(\"vqc_classifier.model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "reverse-shaft",
   "metadata": {},
   "source": [
    "Next, we want to alter the model in a way it can be trained further and on another simulator. To do so, we set the `warm_start` property. When it is set to `True` and `fit()` is called again the model uses weights from previous fit to start a new fit. We also set the `sampler` property of the underlying network to the second instance of the `Sampler` primitive we created in the beginning of the tutorial. Finally, we create and set a new optimizer with `maxiter` is set to `80`, so the total number of iterations is `100`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "accessible-cowboy",
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded_classifier.warm_start = True\n",
    "loaded_classifier.neural_network.sampler = sampler2\n",
    "loaded_classifier.optimizer = COBYLA(maxiter=80)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "revised-bruce",
   "metadata": {},
   "source": [
    "Now we continue training our model from the state we finished in the previous section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "metric-cyprus",
   "metadata": {
    "nbsphinx-thumbnail": {
     "output-index": 0
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<qiskit_machine_learning.algorithms.classifiers.vqc.VQC at 0x1f3c89f50a0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loaded_classifier.fit(train_features, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bronze-spread",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train score 0.8\n",
      "Test score 0.8\n"
     ]
    }
   ],
   "source": [
    "print(\"Train score\", loaded_classifier.score(train_features, train_labels))\n",
    "print(\"Test score\", loaded_classifier.score(test_features, test_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "apparent-bloom",
   "metadata": {},
   "source": [
    "Let's see which data points were misclassified. First, we call `predict` to infer predicted values from the training and test features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "catholic-norway",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_predicts = loaded_classifier.predict(train_features)\n",
    "test_predicts = loaded_classifier.predict(test_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "guided-croatia",
   "metadata": {},
   "source": [
    "Plot the whole dataset and the highlight the points that were classified incorrectly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "tested-handling",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1f3cab11ac0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# return plot to default figsize\n",
    "plt.rcParams[\"figure.figsize\"] = (6, 4)\n",
    "\n",
    "plot_dataset()\n",
    "\n",
    "# plot misclassified data points\n",
    "plt.scatter(\n",
    "    train_features[np.all(train_labels != train_predicts, axis=1), 0],\n",
    "    train_features[np.all(train_labels != train_predicts, axis=1), 1],\n",
    "    s=200,\n",
    "    facecolors=\"none\",\n",
    "    edgecolors=\"r\",\n",
    "    linewidths=2,\n",
    ")\n",
    "plt.scatter(\n",
    "    test_features[np.all(test_labels != test_predicts, axis=1), 0],\n",
    "    test_features[np.all(test_labels != test_predicts, axis=1), 1],\n",
    "    s=200,\n",
    "    facecolors=\"none\",\n",
    "    edgecolors=\"r\",\n",
    "    linewidths=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "genuine-preference",
   "metadata": {},
   "source": [
    "So, if you have a large dataset or a large model you can train it in multiple steps as shown in this tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acknowledged-freight",
   "metadata": {},
   "source": [
    "## 4. PyTorch hybrid models\n",
    "\n",
    "To save and load hybrid models, when using the TorchConnector, follow the PyTorch recommendations of saving and loading the models. For more details please refer to the [PyTorch Connector tutorial](05_torch_connector.ipynb) where a short snippet shows how to do it.\n",
    "\n",
    "Take a look at this pseudo-like code to get the idea:\n",
    "```python\n",
    "# create a QNN and a hybrid model\n",
    "qnn = create_qnn()\n",
    "model = Net(qnn)\n",
    "# ... train the model ...\n",
    "\n",
    "# save the model\n",
    "torch.save(model.state_dict(), \"model.pt\")\n",
    "\n",
    "# create a new model\n",
    "new_qnn = create_qnn()\n",
    "loaded_model = Net(new_qnn)\n",
    "loaded_model.load_state_dict(torch.load(\"model.pt\"))\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "persistent-combine",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3>Version Information</h3><table><tr><th>Software</th><th>Version</th></tr><tr><td><code>qiskit</code></td><td>1.4.3</td></tr><tr><td><code>qiskit_machine_learning</code></td><td>0.9.0</td></tr><tr><th colspan='2'>System information</th></tr><tr><td>Python version</td><td>3.12.9</td></tr><tr><td>OS</td><td>Windows</td></tr><tr><td colspan='2'>Fri Jul 18 15:39:28 2025 Eastern Daylight Time</td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of a Qiskit project</h3><p>&copy; Copyright IBM 2017, 2025.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tutorial_magics\n",
    "\n",
    "%qiskit_version_table\n",
    "%qiskit_copyright"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Tags",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
